physiology-based pk models vs. population pk approaches during drug...

Physiology-based PK models vs. Population PK approaches during drug development

Italo PoggesiJanssen Global Clinical PharmacologyItaly

Take-home messages

• Both PBPK and compartmental NONMEM modelsare applied throughout the whole drugdevelopment

• They have different aims, complementary

– popPK mostly descriptive

– PBPK mostly predictive

• They are characterized by a different knowledge base

– PBPK not fully exploited as yet

• All PK models are grounded to physiology, so thatboth PBPK and NONMEM can be used for predictivepurposes

2ICPAD Madrid 2017 Sept 13-14

Outline

•Principles, differences and history

•Applications of PBPK and NONMEM

–correlation with compartmental models

–FIM

–DDI,

–special populations

•Conclusions

Erdafitinib GCP review meeting July 13, 2017

Principles of popPK (slang: better NONMEM, which also identifies the tool)

• Empirical compartmental models

• NONMEM allows, via definition

of assumptions:• For all subj the same PK model;• distribution of parameters,

the simultaneous description of all data

• With NONMEM we identify fixed effects (value of parameters and dependency on covariates) and random effects (between and within subject, interoccasion variability, etc.)

• Since we are dealing with both fixed and random effectsmixed effect (NONMEM)

),0()1()]exp([)( 2

, NtV

CL

V

DosetC jij

i

i

i

ji

)exp(

),0()exp( 2

CL

Vpop

CLCLi

NVVi

Subject 1:CLpop+1CL

Vpop+1V

Subject 2: CLpop+2

Vpop+2

Population mean: CLpop

Vpop

1V : between subj var

2V

1,t1 : within subj var


Principles of NONMEM

• In this case data are analysed all together, formalizing :

–A structural PK model : e.g., monoexp in this case

–A statistical model for the variance to partitionrandom effect (i.e. between and within subject, interoccasionvariability, etc.) and fixedeffect (i.e. effect of gender, age, etc.)

),0()1()]exp([)( 2

, NtV

CL

V

DosetC jij

i

i

i

ji

)exp(

),0()exp( 2

CL

Vpop

CLCLi

NVVi

Subject 1:CLpop+1CL

Vpop+1V

Subject 2: CLpop+2

Vpop+2

Population mean: CLpop

Vpop

1V : between subj var

2V

1,t1 : within subj var


Principles of PBPK modeling

• Compartmental model implementing interconnectivityand anatomical features : for all relevant tissues and organs(compartment) a mass balance equation is written

• Disposition is described by:

– Partition (all tissues, distribution)

– Clearance (some tissue, elimination)

TissueEliminated

drug

Tissue

Arterial

pool

Venous

pool

Oraldose

IV dose



CT Drug concentration for tissue T

Cinput Drug concentration in input

QT Blood flow

VT Tissue volume

Pt:p Tissue-plasma partition

coefficient

B:P Blood to plasma ratio

CLT Elimination clearance (CLT = 0 in

non-eliminating tissues)

inputT

PB

PT

TTinputT

T

T CCL

P

P

CQCQ

Vdt

dC

:

:

1

Oraldose

IV dose



9

Tissue partition

Clearance epatica

Renal clearance

Oraldose

IV dose

Hepatic clearance


In silico/in vitroin vivo


Log P, pKa, fu

CLint,H, CLR, fu/fuT

In silico/in vitroin vivo

- Comparison with observations

- Modulation of the basic parameters


Comparison PBPK/NONMEM

Mixed-effect modelsPBPK

Number of published papers; keywords:

PBPK NONMEM OR population PK

0

1000

2000

3000

4000

5000

6000

7000

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

Poggesi et al EODMT, 2014



The most obvious one:

• PBPK, based on mechanisticcompartmental models, isredundant. There are manyparameters to be fixed or estimated; at the very least :

– The tissue partition coefficientsfor each compartment

– the clearance terms for eacheliminating organ

• We rarely have information to identify parameters of tissuepartition (and surely not for alltissues)

• NONMEM, based on empiricalcompartmental models, iseconomical. There are fewparameters: in most cases twofor each exponential phase

• The number of parameters istypically the minimum requiredto explain the features of the available data



PBPK• Knowledge-driven

– Science-constrained

Mixed-effect models• Data-driven

– Information-constrained





• Predictive in nature – descriptive on validation



• Descriptive in nature – Predictive on validation






• Relatively applicable for describing individualbehaviours– Relatively precise for

variability (no generallyaccepted statisticalcriteria); however attempts to include MEM




• Applicable for describingindividual behaviours

– Appropriate for describing variabilitysources






• Questionableapplicability for individual behaviour– Relatively precise for

variability (no generallyaccepted statisticalcriteria); however attempts to include MEM

• Hypothesis generation




• Applicable for describingindividual behaviour

– Appropriate for describing variabilitysources

• Hypothesis confirmation


• It is possible to simplify the PBPK model, lumping different tissues which have similar kinetic characteristics

• A lumping approach wasproposed that, based on a minimal model, established a direct link from PBPK to simple compartment models, enabling a more mechanistic interpretation of the empirical compartment models.

Pilari and Huisinga JPKPD 2010



• The proposed lumping is basedon the concentrationnormalization by tissuepartition coefficient




Applications


Applications of NONMEM: prediction of FIM

• Application of allometry in a NONMEM setting

• Relatively simple case:

– sumatriptan; similar proteinbinding, similar metabolicpathways across species

• Allometric equation includingweight and brain weight

• Simultaneous estimation of exponents and coefficients

• Potential for inclusion of covariates (eg pregnancy, in this case)

• Problematic scientific grounds Cosson et al JPKPD 1997


Applications of PBPK: prediction of FIM

• PhRMA and Orbito initiatives

• Blinded analyses:

–Decent prediction of IV PK (69% PhRMA medium to high degree of accuracy, 52.9% AUC within 2-fold OrBiTo)

–Predictions less satisfactory for oral PK (23% PhRMA, 37.2% OrBiTo)

–CL on average overestimated in PhRMA, underestimated in OrBiTo

–PBPK did not appear to be more accurate than allometry

–PBPK crucial to understand assumptions and limitations in scaling approaches, in particular for defining compound classes they can be applied to.

Margolskee et al EJPS 2017Poulin et al JPS 2011


4343,,

,

,

,

1

1

ACYPACYPinhibHi

Hi

absinhiboral

absoral

oral

inhiboral

IRCRCL

CL

fCL

fCL

AUC

AUC

Fractional ratio of CYP3A4 to oral CL(reaction phenotyping) Time-averaged apparent

inhibition ratio =

Ohno et al CPK 2007

Applications of NONMEM: prediction of DDI


• DDI profile: drug A as perpetrator

• Effect of Drug A at 30 and 60 mg/day on midazolam PK: median increase of AUC: 1.77 and 4.97

• Data modeled to have drug A IR,CYP3A4=f(dose)


GW823296 dose (mg)

0 5 10 15 20 25 30 35 40 45 50 55 60 65

mid

azo

lam

AU

Ci/A

UC

0

1

2

3

4

5

6

7

Drug A dose (mg)

• DDI profile: drug A as perpetrator

simvastatin lovastatin buspirone nisoldipine triazolam midazolam felodipine cyclospor nifedipine alprazolam atorvastat telithro zolpidem cerivastat

5 1.01 1.01 1.01 1.01 1.01 1.01 1.01 1.01 1.01 1.01 1.01 1.01 1.00 1.00

10 1.06 1.06 1.06 1.05 1.05 1.05 1.05 1.05 1.04 1.04 1.04 1.03 1.02 1.01

15 1.15 1.15 1.15 1.15 1.14 1.14 1.13 1.12 1.12 1.11 1.10 1.07 1.06 1.02

20 1.31 1.31 1.30 1.29 1.28 1.28 1.27 1.23 1.23 1.21 1.19 1.13 1.10 1.04

25 1.54 1.54 1.53 1.50 1.48 1.47 1.45 1.39 1.37 1.35 1.31 1.21 1.16 1.07

30 1.85 1.85 1.83 1.79 1.75 1.73 1.69 1.58 1.56 1.53 1.45 1.29 1.23 1.09

35 2.27 2.27 2.24 2.16 2.08 2.06 1.99 1.81 1.77 1.72 1.61 1.38 1.29 1.11

40 2.82 2.82 2.77 2.63 2.50 2.46 2.35 2.07 2.01 1.94 1.78 1.46 1.35 1.13

45 3.53 3.53 3.45 3.21 3.00 2.94 2.76 2.35 2.27 2.16 1.95 1.54 1.40 1.15

50 4.46 4.46 4.31 3.92 3.59 3.50 3.23 2.64 2.53 2.39 2.12 1.61 1.45 1.16

55 5.69 5.69 5.43 4.79 4.28 4.14 3.75 2.94 2.80 2.62 2.28 1.68 1.49 1.17

60 7.41 7.41 6.97 5.90 5.12 4.90 4.35 3.25 3.08 2.85 2.43 1.74 1.53 1.18

fold increase of substrate AUCi/AUCGW823296

dose (mg)

Drug A




Del Bene et al PAGE 2015


• DDI profile: bedaquiline as victim. Based on the NONMEM model it was possible to describe typical behavior and variability

Applications of PBPK: prediction of DDI


Zhao et al CPT 2011

Applications of PBPK: prediction of DDI

• 19 labels in which PBPK informed DDI: ibrutinib, rivaroxaban, simeprevire, rilpivirine

• Mostly drug-druginteractions

• Used to predict‘complex’ interactions

29Erdafitinib GCP review meeting July 13, 2017

Ibrutinib predictions,De Zwart CPT 2016

Applications of PBPK: special populations

• Database developed for population of subjects with cancer

• Allowed to estimate correctlymidazolam PK changes in thispopulation

• Cons: no change in drug metabolizingenzymes activity


Cheeti et al Biopharm Drug Disp 2013

parameter Change

Age

CRCL

Hematocrit

Albumin

AAG

All PK models have physiological bases(as PK parameters have physiological bases)

Approaches to PK


All PK models have physiological bases(as PK parameters have physiological bases)

Approaches to PK


CL =

𝑖

𝐶𝐿𝑖

• Rowland M, et al. Clearance concepts in

pharmacokinetics. JPKPD. 1973

• Wilkinson GR, Shand DG. A physiological

approach to hepatic drug clearance. CPT

1975.

Many recent papers, eg.Berezhkovskiy JPKPD 2004Lombardo et al J Med Chem 2002, 2004Poulin&Theil JPS 2000, 2002Roger et al. JPS 2005, 2006

Conclusions

• All PK models are grounded to physiology, so thatboth NONMEM and PBPK can be used for predictiveaims (but we need the more complex PBPK description to deal with complex cases)

• Both NONMEM and PBPK are applied throughoutthe whole development process

• They have different aims, complementary

–popPK descriptive/predictive

–PBPK predictive/descriptive

• Different knowledge base

–PBPK not fully exploited as yet


physiology-based pk models vs. population pk approaches during drug...

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